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In this study, the inverse kinematics solution of a 7-DOF redundant robot manipulator was performed by using firefly algorithm that is a swarm optimization technique. In order to show the power of this technique, a redundant robotic arm which is inadequate inverse kinematic solution by conventional methods has been chosen. Both speed and accuracy are two important factors in robotic studies. For this reason, the comparison of the method used in this study in terms of speed and accuracy has been carried out in depth. The scenario used is as follows: Firstly, the position equations of this manipulator are derived with the DH parameters. Afterward, the position of the end effector is obtained in the work space according to the forward kinematic calculation. Finally, the joint angles that will be directed to the calculated position values with the least error are obtained by the firefly algorithm and the obtained result is compared with other swarm algorithms such as particle swarm optimization and artificial bee colony.

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This paper presents an artificial bee colony algorithm for solving the inverse kinematics of 7-degree-of-freedom robotic arm which has been newly designed and not used in the literature. The kinematics analysis of this manipulator which has an excessive number of joints, is quite complex. In this study, artificial bee colony, which is one of the swarm-based heuristic algorithms, has been used for inverse kinematics solution and its results have been analyzed in terms of position error and calculation time. In order to ensure the accuracy of the algorithm, calculations have been also carried out in 100 different points selected from the workspace of the robot manipulator. The results have been compared with particle swarm optimization, which is another swarm algorithm in terms of position error and computation time. The results obtained by computer simulation clearly show that the artificial bee colony algorithm produces effective results compared with the literature.

This study has been inspired by golf ball movements during the game to improve particle swarm optimization. Because, all movements from the first to the last move of the golf ball are the moves made by the player to win the game. Winning this game is also a result of successful implementation of the desired moves. Therefore, the movements of the golf ball are also an optimization, and this has a meaning in the scientific world. In this sense, the movements of the particles in the PSO algorithm have been associated with the movements of the golf ball in the game. Thus, the velocities of the particles have converted to parabolically descending structure as they approach the target. Based on this feature, this meta-heuristic technique is called RDV (random descending velocity) IW PSO. In this way, the result obtained is improved thousands of times with very small movements. For the application of the proposed new technique, the inverse kinematics calculation of the 7-joint robot arm has been performed and the obtained results have been compared with the traditional PSO, some IW techniques, artificial bee colony, firefly algorithm and quantum PSO.